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Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Published on 2013-01-163675 Views
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closel
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Presentation
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization00:00
Regularized Loss Minimization (1)00:07
Regularized Loss Minimization (2)00:28
Dual Coordinate Ascent (DCA)00:58
SDCA vs. SGD - update rule02:16
SDCA vs. SGD - update rule - Example03:10
SDCA vs. SGD - experimental observations (1)03:33
SDCA vs. SGD - experimental observations (2)04:23
SDCA vs. SGD - Current analysis is unsatisfactory04:45
Dual vs. Primal sub-optimality06:24
Our results08:16
SDCA vs. DCA - Randomization is crucial10:37
Smoothing the hinge-loss (1)11:32
Smoothing the hinge-loss (2)12:15
Smoothing the hinge-loss (3)13:26
Additional related work13:43
Extensions16:33
Proof Idea (1)17:13
Proof Idea (2)17:16
Summary17:17